SOTAVerified

Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

Showing 251275 of 1718 papers

TitleStatusHype
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team CompositionCode1
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online AdvertisingCode1
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
CoLight: Learning Network-level Cooperation for Traffic Signal ControlCode1
Collaborative Visual NavigationCode1
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level OptimizationCode1
Cooperative Policy Learning with Pre-trained Heterogeneous Observation RepresentationsCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Curriculum Learning With Counterfactual Group Relative Policy Advantage For Multi-Agent Reinforcement LearningCode1
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight DeliveryCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Collaborating with Humans without Human DataCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement LearningCode1
Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V CommunicationCode1
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement LearningCode1
Group-Aware Coordination Graph for Multi-Agent Reinforcement LearningCode1
ELIGN: Expectation Alignment as a Multi-Agent Intrinsic RewardCode1
Energy-based Surprise Minimization for Multi-Agent Value FactorizationCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Laser Learning Environment: A new environment for coordination-critical multi-agent tasksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified